1. Field of the Invention
The field of the invention relates to x-ray scanners and, more particularly, to multi-source inverse geometry CT (“IGCT”) systems.
2. Description of Related Art
Most modern CT scanners are based on a 3rd generation architecture, which embodies a single X-ray source and a large X-ray detector. The X-ray detector can be a 1-dimensional—usually curved-array of detector cells, resulting in fan-beam geometry. In axial scans (i.e. the patient table does not move during the gantry rotation) the result is a purely planar dataset to which two-dimensional (“2D”) filtered backprojection (FBP) can be applied. Reconstruction is theoretically exact, and any possible image artifacts may come from physical limitations of the scanner, such as quantum noise, aliasing, beam hardening, and scattered radiation.
In the early 1980's, helical (or spiral) CT systems were introduced. Such systems acquired data faster by translating the patient table during the gantry rotation. In a helical CT system, the raw CT data is typically interpolated to 2D planar datasets as if it was acquired without table translation, and 2D FBP is applied.
Since about 1990, multi-slice or multi-detector-row CT systems have become the standard CT architecture for premium medical scanners: the detector has multiple rows, i.e. a 2-dimensional array of detector cells, resulting in cone-beam geometry. Since these geometries do not result in planar datasets, 2D image reconstruction algorithms will not be based on the correct scan geometry and may result in cone-beam artifacts. For axial scan mode, Feldkamp, Davis, and Kress proposed a three-dimensional (“3D”) cone-beam reconstruction algorithm (“FDK algorithm”) that adapts 2D fan-beam filtered backprojection (FBP) to cone-beam geometry. The FDK algorithm works well near the mid-plane and near the center of rotation, but artifacts occur and get worse as the cone angle increases. For 40 mm-coverage scanners (which typically corresponds to about a 4 degree cone angle) significant artifacts occur, particularly towards the z=−20 mm and z=20 mm slices. The raw CT data is actually fundamentally incomplete in 3D axial scans, and therefore, even the best thinkable algorithm will result in artifacts in some cases.
On the other hand, in helical cone-beam scans, the data is fundamentally complete (provided the table speed is not too high compared to the gantry rotation speed and the slice thickness) and therefore exact reconstruction is possible. The FDK algorithm has been adapted for helical scan modes, but results in non-exact or approximate reconstruction. Accordingly, exact 3D helical cone-beam reconstruction algorithms (“Katsevitch algorithms”) have been developed, which perform filtering operations along special filter lines followed by backprojection. Disadvantages associated with the Katsevitch algorithms are that such algorithms assume the detector surfaces are continuously sampled, and that such algorithms have other associated limitations.
Another disadvantage associated with the fan-beam and cone-beam geometries discussed above is that each type of geometry has a limited field of view (“FOV”). In a fan-based geometry, the FOV is an area of a scannable object that constantly receives an x-ray beam as the source and detector rotate around the scannable object. At some image voxels outside the FOV, the projection data is incomplete. Consequently, the size of the FOV and how many artifacts (if any) it contains are important, the goal being to make the FOV as large as possible and as free of as many artifacts as possible. In conventional CT systems, the size of the FOV is proportional to the trans-axial size of the x-ray detector. The larger the detector, the larger the FOV will be, and vice versa. Increasing the size of the detector makes the FOV larger but is technically difficult and costly to implement.
As an alternative to using a larger detector to cover the field of view (FOV), an Inverse Geometry computed tomography (IGCT) system 100 (shown in FIG. 1) has been developed that uses a small detector 101 combined with a large distributed source 102, on which multiple x-ray point sources 103 are arrayed trans-axially (in the xy-plane) and longitudinally (along the z-axis). Each x-ray point source 103 emits a fan-beam (or a cone-beam) 104 at different times, and the projection data (e.g., sinograms) 105 is captured by the detector 101. Additionally, the detector 101, the distributed source 102, and the fan beams (or cone-beams) 104 may be axially rotated about a rotational axis 107. The projection data 105 captured by the detector 101 is processed to reconstruct an object of interest within the field of view 106. A known rebinning algorithm may be used to rebin the projection data into parallel ray projections.
Trans-axially, the multiple x-ray point sources 103 are positioned preferably on an iso-centered arc so that all corresponding fan beams (or cone beams) 104 can be rotated to fit into conventional 3rd generation system with an iso-focused detector. This makes exact re-binning to full cone beams possible and also helps to achieve a uniform beam profile. The resulting dataset can be re-arranged or re-binned into multiple longitudinally offset third-generation datasets. An algorithm developed for multiple x-ray point sources 103 distributed in z can also be applied to multiple longitudinally-offset axial scans with a conventional 3rd generation CT, and vice versa. While positioning the sources on isocentric arcs is desirable for these reasons, other arrangements, such as detector-centered arcs and flat arrays, can also be used.
An embodiment of multi-source projection data re-binning is shown in FIGS. 2 and 3. In FIG. 2, a multi-source IGCT system 200 includes a single x-ray detector array 201 and a plurality of x-ray point sources 203, which are arranged along an iso-centered arc at a predetermined radius from the x-ray detector 201. In use, each x-ray point source 203 projects a beam 202, 204 onto the detector 201. Each beam 202, 204 creates a sinogram (not shown). Thus, as can be appreciated from FIG. 2, raw projection data from the multi-source IGCT system 200 comprises a group of sinograms that are generated by the x-ray point sources 203.
In FIG. 3, a conventional 3rd Generation CT system 300 is shown that comprises a single x-ray source 303 positioned at a predetermined distance from at least two detector arrays 301 that are aligned along an iso-centered arc. Comparing FIGS. 2 and 3, it is seen that the beam 202 in the IGCT system 200 corresponds to the beam 302 in the 3rd Generation CT system 300, and that the beam 204 in the IGCT system 200 corresponds to the beam 304 in the 3rd Generation CT system 300. Consequently, sinograms that correspond to the beams 202, 204 in FIG. 2 and that are in the same trans-axial plane may be combined and re-arranged by a re-binning process. When each x-ray point source 203 on the same trans-axial plane is positioned on the same iso-centered circle (e.g. shifted to the point occupied by the single point source 303 in FIG. 3), exact rebinning is possible, As FIG. 3 illustrates, the resulting re-binned sinogram will exactly correspond to a sinogram from a 3rd generation system having multiple flat panel detectors 301 positioned along an iso-centered circle 306.
Multi-source IGCT 3D rebinning may use any of the following three techniques (or combinations thereof): (1) z-rebinning, (2) trans-axial (xy) rebinning, and (3) feathering between sub-views, each of which is further described below.
The z re-binning technique re-bins the IGCT projection data for example to a source-focused-detector geometry. For example, each sinogram may be rebinned using 1D linear interpolation with extrapolation. Depending on the new source-to-iso-center distance, a larger detector height may be required to capture all the information.
The trans-axial (xy) rebinning technique further rebins the IGCT projection data to a third generation geometry with a source-focused detector. To perform trans-axial rebinning, the angle and the distance from center for each ray are computed and interpolated into the desired geometry.
A process called “feathering” is used in situations where some mismatch may occur between measurements at the edge of the detector array across neighboring sub-sinograms. To mitigate this discontinuity, a slightly larger detector can be used, such that there is some overlap between neighboring sub-sinograms. The overlapping channels are multiplied with linearly decreasing/increasing weights and added together with the weighted channels from the adjacent sub-sinograms.
FIG. 4 illustrates multi-source IGCT projection data 401 that is re-binned and/or feathered to produce a re-binned 3rd generation sinogram 402.
After the re-binning described above has been performed, the resulting multiple 3rd generation sinograms are associated with x-ray point sources 103 offset along the z-axis (as illustrated in FIG. 5). Therefore, any conventional 3D cone-beam reconstruction algorithm—such as FDK—can be used to reconstruct each of the 3rd generation datasets. With multiple sinograms from different sources in z-axis, combining this projection data results in better reconstructions as compared to a single third-generation dataset. This is especially true since cone beam artifacts due to data insufficiency may be reduced using additional information from longitudinally offset data. Also, with multiple sources distributed in z, the scan coverage can be increased without sacrificing image quality due to those cone-beam artifacts.
FIG. 6 is a diagram 600 illustrating a method for reconstructing the re-binned multi-slice IGCT projection data of FIG. 5. Referring to FIG. 6, one way to combine the sinogram information to produce more accurate reconstructions is to combine the data in the image domain. First the slices 601, 602, 603, 604, 605, 606, 607, 608, and 609 corresponding to each dataset (each source z location) are reconstructed, with one set of slices (or reconstructed volume) for each dataset. In FIG. 6, each dataset 610, 620, 630 comprises three sets of slices. For example, a first dataset 610 comprises slices 601, 602, and 603. A second dataset 620 comprises slices 604, 605, and 606. A third dataset 630 comprises slices 607, 608, and 609. These three datasets are then combined by using only the slice from the nearest source, or by applying a weighted average. For example, with three sources along the z-axis, the reconstruction from the center source will suffer from cone-beam artifacts at the top slices and the bottom slices. But the reconstruction volume from the top source will provide artifact-free top slices, and analogous for the bottom source. So essentially, in this embodiment, for every z-position, the best slices 601, 605, 609 from the three reconstruction volumes associated with each source are chosen and combined into a single volume 640.
Unless corrected, this approach has several unfavorable properties. First, the geometry has asymmetric cone angles. Considering three linearly adjacent x-ray point sources for purposes of illustration, outermost x-ray point sources will each have a cone angle that is twice as large as for the center x-ray point source. Second, some portions of the volume will receive more x-ray radiation than others, and some of that dose will be used inefficiently and the image noise will be non-uniform.
Still needed are methods for increasing FOV in IGCT systems without sacrificing image quality. Embodiments of such methods should ideally also provide uniform cong angles, a uniform flux distribution throughout a scanned region of interest, and uniform image noise.